title: Cluster analysis of clinical data identifies fibromyalgia subgroups.
abstract: BACKGROUND
  Fibromyalgia (FM) is mainly characterized by widespread pain and multiple accompanying symptoms, which hinder FM assessment and management. In order to reduce FM heterogeneity we classified clinical data into simplified dimensions that were used to define FM subgroups.
  
  
  METHODS
  48 variables were evaluated in 1,446 Spanish FM cases fulfilling 1990 ACR FM criteria. A partitioning analysis was performed to find groups of variables similar to each other. Similarities between variables were identified and the variables were grouped into dimensions. This was performed in a subset of 559 patients, and cross-validated in the remaining 887 patients. For each sample and dimension, a composite index was obtained based on the weights of the variables included in the dimension. Finally, a clustering procedure was applied to the indexes, resulting in FM subgroups.
  
  
  RESULTS
  VARIABLES CLUSTERED INTO THREE INDEPENDENT DIMENSIONS: "symptomatology", "comorbidities" and "clinical scales". Only the two first dimensions were considered for the construction of FM subgroups. Resulting scores classified FM samples into three subgroups: low symptomatology and comorbidities (Cluster 1), high symptomatology and comorbidities (Cluster 2), and high symptomatology but low comorbidities (Cluster 3), showing differences in measures of disease severity.
  
  
  CONCLUSIONS
  We have identified three subgroups of FM samples in a large cohort of FM by clustering clinical data. Our analysis stresses the importance of family and personal history of FM comorbidities. Also, the resulting patient clusters could indicate different forms of the disease, relevant to future research, and might have an impact on clinical assessment.
authors.affiliation: Genomics and Disease Group, Centre for Genomic Regulation (CRG), Barcelona, Spain ; Universitat Pompeu Fabra (UPF), Barcelona, Spain ; Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.
authors.forename: Elisa
authors.initials: E
authors.lastname: Docampo
authors.forename: Antonio
authors.initials: A
authors.lastname: Collado
authors.forename: Geòrgia
authors.initials: G
authors.lastname: Escaramís
authors.forename: Jordi
authors.initials: J
authors.lastname: Carbonell
authors.forename: Javier
authors.initials: J
authors.lastname: Rivera
authors.forename: Javier
authors.initials: J
authors.lastname: Vidal
authors.forename: José
authors.initials: J
authors.lastname: Alegre
authors.forename: Raquel
authors.initials: R
authors.lastname: Rabionet
authors.forename: Xavier
authors.initials: X
authors.lastname: Estivill
mesh_terms: D000704:Analysis of Variance; D016000:Cluster Analysis; D015897:Comorbidity; D005356:Fibromyalgia / Q000145:classification* / Q000453:epidemiology*; D006801:Humans; D013030:Spain / Q000453:epidemiology